This is a data set that includes the following variables:
| DH1_W_Responsible |
| DH2_W_Civilized |
| DH3_W_Moral |
| DH4_W_Polite |
| DH5_W_Childlike |
| DH6_W_Rational |
| DH7_W_Warm |
| DH8_W_Agentic |
| DH9_W_Refined |
| DH10_W_Lacking_Culture |
| DH11_W_Lacking_Self-restraint |
| DH12_W_Instinctual |
| DH13_W_Mature |
| DH14_W_Stoic |
| DH15_W_Emotionally_Responsive |
| DH16_W_Cold |
| DH17_W_Open |
| DH18_W_Rigid |
| DH19_W_Passive |
| DH20_W_Superficial |
| DH1_N_Responsible |
| DH2_N_Civilized |
| DH3_N_Moral |
| DH4_N_Polite |
| DH5_N_Childlike |
| DH6_N_Rational |
| DH7_N_Warm |
| DH8_N_Agentic |
| DH9_N_Refined |
| DH10_N_Lacking_Culture |
| DH11_N_Lacking_Self-restraint |
| DH12_N_Instinctual |
| DH13_N_Mature |
| DH14_N_Stoic |
| DH15_N_Emotionally_Responsive |
| DH16_N_Cold |
| DH17_N_Open |
| DH18_N_Rigid |
| DH19_N_Passive |
| DH20_N_Superficial |
| No_Columbus |
| Create_IPD |
| BorninUS |
| LengthState |
| FatherUSBorn |
| MotherUSBorn |
| PrimaryCaretaker |
| PrimaryEd |
| SecondCaretaker |
| SecondEdu |
| Religion |
| Religiosity |
| College |
| Year |
| Education |
| LibCon |
| SES |
| Race |
| StateNumeric |
| LongestRegion |
| SubjectNumber |
| EMP_comp |
| IMP_comp |
| N_Uncivilized |
| White_Dehumanize |
| Native_Dehumanize |
| Age |
It’s primary focus is to look a bit more closely at the dehumaniztion variables and how they interact not only with one another, but with the dependent variables (Columbus Day/Indigenous Peoples’ Day (henceforth referred to as IPD)), as well as some of the demographic information gathered on the participants in this sample.
This sample comes from Study Two, so it is (if I’m not mistaken) a sample of participants gathered via mturk.
First, lets take a look at a descriptives table of our variables. Keep in mind that some of the variables are categorical and are more difficult to interpret in a descriptives table.
| n | mean | sd | median | se | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 2811 | 61.24 | 21.82 | 62 | 0.4116 |
| DH2_W_Civilized | 2810 | 65.89 | 23.38 | 69 | 0.4411 |
| DH3_W_Moral | 2812 | 55.77 | 22.76 | 55 | 0.4292 |
| DH4_W_Polite | 2812 | 57.99 | 22.53 | 58 | 0.4248 |
| DH5_W_Childlike | 2809 | 40.76 | 24.49 | 43 | 0.462 |
| DH6_W_Rational | 2810 | 58.23 | 22.49 | 57 | 0.4243 |
| DH7_W_Warm | 2810 | 58.27 | 22.32 | 58 | 0.421 |
| DH8_W_Agentic | 2800 | 46.11 | 22.34 | 50 | 0.4222 |
| DH9_W_Refined | 2809 | 52.87 | 22.26 | 52 | 0.4199 |
| DH10_W_Lacking_Culture | 2808 | 46.34 | 27.71 | 50 | 0.523 |
| DH11_W_Lacking_Self-restraint | 2808 | 48.59 | 25.58 | 50 | 0.4828 |
| DH12_W_Instinctual | 2810 | 51.47 | 22.98 | 51 | 0.4335 |
| DH13_W_Mature | 2809 | 57.34 | 22.31 | 56 | 0.421 |
| DH14_W_Stoic | 2808 | 42.88 | 22.31 | 49 | 0.4209 |
| DH15_W_Emotionally_Responsive | 2812 | 59.98 | 22.77 | 60 | 0.4294 |
| DH16_W_Cold | 2810 | 44.74 | 23.99 | 49 | 0.4525 |
| DH17_W_Open | 2811 | 55.59 | 22.81 | 55 | 0.4302 |
| DH18_W_Rigid | 2807 | 49.6 | 23.27 | 50 | 0.4393 |
| DH19_W_Passive | 2810 | 45.16 | 23.21 | 49 | 0.4378 |
| DH20_W_Superficial | 2810 | 59.32 | 25.54 | 60 | 0.4818 |
| DH1_N_Responsible | 2807 | 63.86 | 21.06 | 64 | 0.3976 |
| DH2_N_Civilized | 2806 | 64.58 | 21.97 | 65 | 0.4147 |
| DH3_N_Moral | 2807 | 65.75 | 20.88 | 66 | 0.3941 |
| DH4_N_Polite | 2806 | 63.22 | 21.42 | 63 | 0.4044 |
| DH5_N_Childlike | 2802 | 29.22 | 22.26 | 25 | 0.4205 |
| DH6_N_Rational | 2807 | 61.61 | 21.17 | 61 | 0.3995 |
| DH7_N_Warm | 2804 | 61.05 | 22.07 | 60 | 0.4167 |
| DH8_N_Agentic | 2800 | 44.97 | 22.32 | 50 | 0.4219 |
| DH9_N_Refined | 2806 | 51.11 | 22.44 | 51 | 0.4235 |
| DH10_N_Lacking_Culture | 2804 | 23.9 | 23.15 | 17 | 0.4371 |
| DH11_N_Lacking_Self-restraint | 2805 | 34.44 | 23.39 | 33 | 0.4416 |
| DH12_N_Instinctual | 2807 | 61.3 | 23.48 | 61 | 0.4433 |
| DH13_N_Mature | 2806 | 65.15 | 20.69 | 65 | 0.3907 |
| DH14_N_Stoic | 2808 | 59.11 | 22.29 | 57 | 0.4206 |
| DH15_N_Emotionally_Responsive | 2806 | 54.38 | 24.29 | 53 | 0.4585 |
| DH16_N_Cold | 2805 | 35.19 | 22.8 | 35 | 0.4306 |
| DH17_N_Open | 2805 | 53.8 | 23.34 | 53 | 0.4406 |
| DH18_N_Rigid | 2807 | 45.41 | 23.89 | 50 | 0.451 |
| DH19_N_Passive | 2805 | 45.03 | 23.79 | 50 | 0.4492 |
| DH20_N_Superficial | 2805 | 30.41 | 22.87 | 27 | 0.4318 |
| No_Columbus | 2858 | 3.793 | 2.125 | 4 | 0.03976 |
| Create_IPD | 2858 | 4.481 | 1.956 | 4 | 0.03658 |
| BorninUS* | 2801 | 1.956 | 0.2041 | 2 | 0.003857 |
| LengthState | 2797 | 30.72 | 12.88 | 29 | 0.2435 |
| FatherUSBorn* | 2801 | 1.893 | 0.3352 | 2 | 0.006334 |
| MotherUSBorn* | 2801 | 1.882 | 0.3337 | 2 | 0.006306 |
| PrimaryCaretaker* | 2800 | 1.282 | 0.7601 | 1 | 0.01436 |
| PrimaryEd | 2801 | 2.988 | 1.225 | 3 | 0.02315 |
| SecondCaretaker* | 2801 | 2.828 | 1.114 | 3 | 0.02105 |
| SecondEdu | 2736 | 2.908 | 1.323 | 2 | 0.02529 |
| Religion* | 2799 | 2.45 | 1.674 | 2 | 0.03164 |
| Religiosity | 2800 | 3.632 | 2.235 | 4 | 0.04224 |
| College* | 2799 | 1.893 | 0.3094 | 2 | 0.005848 |
| Year | 300 | 3.363 | 1.631 | 3 | 0.09417 |
| Education | 2800 | 3.459 | 1.127 | 4 | 0.0213 |
| LibCon | 2799 | 4.401 | 1.713 | 4 | 0.03237 |
| SES* | 2801 | 1.538 | 0.4986 | 2 | 0.009422 |
| Race* | 2903 | 3.42 | 1.684 | 3 | 0.03125 |
| StateNumeric* | 2788 | 24.73 | 14.36 | 25 | 0.272 |
| LongestRegion* | 2789 | 4.425 | 2.104 | 5 | 0.03984 |
| SubjectNumber | 2903 | 1452 | 838.2 | 1452 | 15.56 |
| EMP_comp | 2841 | 3.337 | 1.438 | 3.4 | 0.02698 |
| IMP_comp | 2841 | 5.613 | 1.208 | 5.8 | 0.02266 |
| N_Uncivilized | 2811 | 2.277 | 1.33 | 2 | 0.02509 |
| White_Dehumanize | 2793 | 52.93 | 12.03 | 53.25 | 0.2275 |
| Native_Dehumanize | 2792 | 50.67 | 11.38 | 51.05 | 0.2153 |
| Age | 2793 | 38.72 | 13.33 | 35 | 0.2523 |
##
## Pearson's product-moment correlation
##
## data: dfdehumanDV$N_Uncivilized and dfdehumanDV$DH5_N_Childlike
## t = 16.686, df = 2800, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2666829 0.3340519
## sample estimates:
## cor
## 0.3007425
At baseline the two seem to be somewhat negatively correlated with scores clustering around the Natives as Civilized and childlike.
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Childlike model uncentered:
##
## Call:
## lm(formula = No_Columbus ~ DH5_N_Childlike + DH5_W_Childlike +
## DH5_N_Childlike * DH5_W_Childlike, data = dv4cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0529 -1.8904 0.0164 1.9077 4.0183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.282e+00 1.014e-01 32.350 < 2e-16 ***
## DH5_N_Childlike -5.454e-03 3.656e-03 -1.492 0.136
## DH5_W_Childlike 1.771e-02 2.295e-03 7.719 1.62e-14 ***
## DH5_N_Childlike:DH5_W_Childlike -4.217e-05 6.721e-05 -0.627 0.530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.093 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.03255, Adjusted R-squared: 0.03151
## F-statistic: 31.38 on 3 and 2798 DF, p-value: < 2.2e-16
…and here it is centered:
##
## Call:
## lm(formula = No_Columbus ~ scale(DH5_N_Childlike, scale = F) +
## scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike,
## scale = F) * scale(DH5_W_Childlike, scale = F), data = dv4cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0529 -1.8904 0.0164 1.9077 4.0183
##
## Coefficients:
## Estimate
## (Intercept) 3.794e+00
## scale(DH5_N_Childlike, scale = F) -7.172e-03
## scale(DH5_W_Childlike, scale = F) 1.648e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) -4.217e-05
## Std. Error
## (Intercept) 4.171e-02
## scale(DH5_N_Childlike, scale = F) 1.944e-03
## scale(DH5_W_Childlike, scale = F) 1.793e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 6.721e-05
## t value
## (Intercept) 90.962
## scale(DH5_N_Childlike, scale = F) -3.689
## scale(DH5_W_Childlike, scale = F) 9.189
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) -0.627
## Pr(>|t|)
## (Intercept) < 2e-16
## scale(DH5_N_Childlike, scale = F) 0.000229
## scale(DH5_W_Childlike, scale = F) < 2e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.530454
##
## (Intercept) ***
## scale(DH5_N_Childlike, scale = F) ***
## scale(DH5_W_Childlike, scale = F) ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.093 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.03255, Adjusted R-squared: 0.03151
## F-statistic: 31.38 on 3 and 2798 DF, p-value: < 2.2e-16
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Civilized model uncentered:
##
## Call:
## lm(formula = No_Columbus ~ N_Uncivilized, data = dv4cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0475 -1.8429 -0.0475 1.9525 4.1797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.25201 0.07889 53.896 < 2e-16 ***
## N_Uncivilized -0.20453 0.02991 -6.838 9.85e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.11 on 2809 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.01637, Adjusted R-squared: 0.01602
## F-statistic: 46.75 on 1 and 2809 DF, p-value: 9.846e-12
…and here it is centered:
##
## Call:
## lm(formula = No_Columbus ~ scale(N_Uncivilized, scale = F), data = dv4cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0475 -1.8429 -0.0475 1.9525 4.1797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78620 0.03979 95.158 < 2e-16 ***
## scale(N_Uncivilized, scale = F) -0.20453 0.02991 -6.838 9.85e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.11 on 2809 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.01637, Adjusted R-squared: 0.01602
## F-statistic: 46.75 on 1 and 2809 DF, p-value: 9.846e-12
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indigenous Peoples’ Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Childlike model uncentered:
##
## Call:
## lm(formula = Create_IPD ~ DH5_N_Childlike + DH5_W_Childlike +
## DH5_N_Childlike * DH5_W_Childlike, data = dv5cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4267 -1.2536 -0.1019 1.6597 3.3779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.253e+00 9.372e-02 45.382 < 2e-16 ***
## DH5_N_Childlike -1.148e-02 3.378e-03 -3.397 0.000691 ***
## DH5_W_Childlike 1.173e-02 2.120e-03 5.535 3.41e-08 ***
## DH5_N_Childlike:DH5_W_Childlike 5.913e-05 6.209e-05 0.952 0.341053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.933 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.02507, Adjusted R-squared: 0.02403
## F-statistic: 23.99 on 3 and 2798 DF, p-value: 2.523e-15
…and here it is centered:
##
## Call:
## lm(formula = Create_IPD ~ scale(DH5_N_Childlike, scale = F) +
## scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike,
## scale = F) * scale(DH5_W_Childlike, scale = F), data = dv5cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4267 -1.2536 -0.1019 1.6597 3.3779
##
## Coefficients:
## Estimate
## (Intercept) 4.467e+00
## scale(DH5_N_Childlike, scale = F) -9.066e-03
## scale(DH5_W_Childlike, scale = F) 1.346e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 5.913e-05
## Std. Error
## (Intercept) 3.854e-02
## scale(DH5_N_Childlike, scale = F) 1.796e-03
## scale(DH5_W_Childlike, scale = F) 1.657e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 6.209e-05
## t value
## (Intercept) 115.909
## scale(DH5_N_Childlike, scale = F) -5.047
## scale(DH5_W_Childlike, scale = F) 8.125
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.952
## Pr(>|t|)
## (Intercept) < 2e-16
## scale(DH5_N_Childlike, scale = F) 4.77e-07
## scale(DH5_W_Childlike, scale = F) 6.65e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.341
##
## (Intercept) ***
## scale(DH5_N_Childlike, scale = F) ***
## scale(DH5_W_Childlike, scale = F) ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.933 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.02507, Adjusted R-squared: 0.02403
## F-statistic: 23.99 on 3 and 2798 DF, p-value: 2.523e-15
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indegenous Peoples’ Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Civilized model uncentered:
##
## Call:
## lm(formula = Create_IPD ~ N_Uncivilized, data = dv5cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8094 -1.0323 0.1906 1.4497 3.7447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.06837 0.07205 70.346 <2e-16 ***
## N_Uncivilized -0.25901 0.02732 -9.481 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.927 on 2809 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.03101, Adjusted R-squared: 0.03067
## F-statistic: 89.9 on 1 and 2809 DF, p-value: < 2.2e-16
…and here it is centered:
##
## Call:
## lm(formula = Create_IPD ~ scale(N_Uncivilized, scale = F), data = dv5cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8094 -1.0323 0.1906 1.4497 3.7447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.47848 0.03634 123.249 <2e-16 ***
## scale(N_Uncivilized, scale = F) -0.25901 0.02732 -9.481 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.927 on 2809 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.03101, Adjusted R-squared: 0.03067
## F-statistic: 89.9 on 1 and 2809 DF, p-value: < 2.2e-16
We can also look at both of these groups side by side:
and…
For Civilized
##
## Call:
## lm(formula = Rating ~ N_Uncivilized + Holiday_Support + N_Uncivilized:Holiday_Support,
## data = dv4and5cor2.lng.cen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8094 -1.8429 -0.0286 1.9525 4.1797
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.47848 0.03810 117.540
## N_Uncivilized -0.25901 0.02864 -9.042
## Holiday_SupportNo_Columbus -0.69228 0.05388 -12.848
## N_Uncivilized:Holiday_SupportNo_Columbus 0.05448 0.04051 1.345
## Pr(>|t|)
## (Intercept) <2e-16 ***
## N_Uncivilized <2e-16 ***
## Holiday_SupportNo_Columbus <2e-16 ***
## N_Uncivilized:Holiday_SupportNo_Columbus 0.179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.02 on 5618 degrees of freedom
## (184 observations deleted due to missingness)
## Multiple R-squared: 0.05034, Adjusted R-squared: 0.04983
## F-statistic: 99.27 on 3 and 5618 DF, p-value: < 2.2e-16
For Childlike
##
## Call:
## lm(formula = Rating ~ DH5_N_Childlike + Holiday_Support + DH5_N_Childlike:Holiday_Support,
## data = dv4and5cor.lng.cen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5808 -1.7851 0.2009 1.6018 3.2646
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.478230 0.038595 116.031
## DH5_N_Childlike -0.003512 0.001734 -2.025
## Holiday_SupportNo_Columbus -0.692719 0.054582 -12.691
## DH5_N_Childlike:Holiday_SupportNo_Columbus 0.002804 0.002453 1.143
## Pr(>|t|)
## (Intercept) <2e-16 ***
## DH5_N_Childlike 0.0429 *
## Holiday_SupportNo_Columbus <2e-16 ***
## DH5_N_Childlike:Holiday_SupportNo_Columbus 0.2530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.043 on 5600 degrees of freedom
## (202 observations deleted due to missingness)
## Multiple R-squared: 0.02868, Adjusted R-squared: 0.02816
## F-statistic: 55.11 on 3 and 5600 DF, p-value: < 2.2e-16
Let’s dig into some of the demographic information in this subset of the data.
First let’s look at the frequency of our different racial groups
| Asian | Black | White | Latino | Middle Eastern | Native | Other | Multiracial |
|---|---|---|---|---|---|---|---|
| 139 | 224 | 2097 | 106 | 4 | 17 | 24 | 292 |
Since our data skews toward Asian, Black, White, and Latino let’s use these groups in future analysis.
First off, let’s look at how our different racial groups answered the question about Columbus Day.
Next let’s see how these groups answered the question about Indigenous Peoples’ Day
For the next set of graphs let’s revisit the question of dehumanization. What we’re interested in here is whether or not responses to the questions of dehumanization differed by racial group.
| Race | N | N_Uncivilized | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 2.748 | 1.373 | 0.1165 | 0.2303 |
| Black | 223 | 2.251 | 1.461 | 0.09784 | 0.1928 |
| White | 2096 | 2.257 | 1.286 | 0.02809 | 0.0551 |
| Latino | 106 | 2.575 | 1.615 | 0.1569 | 0.311 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 40 13.464 7.704 4.01e-05 ***
## Residuals 2560 4474 1.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_Uncivilized ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian -0.497080363 -0.86434038 -0.1298203 0.0028715
## White-Asian -0.491522050 -0.78917824 -0.1938659 0.0001329
## Latino-Asian -0.172729741 -0.61095954 0.2655001 0.7416893
## White-Black 0.005558313 -0.23381818 0.2449348 0.9999237
## Latino-Black 0.324350622 -0.07658236 0.7252836 0.1599987
## Latino-White 0.318792309 -0.01953673 0.6571214 0.0731740
This analysis is done via One way ANOVA using ratings of how civilized Natives are as the DV
Now let’s look at those same tables and bar graphs for ratings of how childlike Natives are:
| Race | N | DH5_N_Childlike | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 35.21 | 22.16 | 1.879 | 3.716 |
| Black | 222 | 23.43 | 23.63 | 1.586 | 3.125 |
| White | 2091 | 29.22 | 21.65 | 0.4735 | 0.9286 |
| Latino | 106 | 32.21 | 26.81 | 2.604 | 5.163 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 13372 4457 9.134 5.19e-06 ***
## Residuals 2554 1246388 488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DH5_N_Childlike ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian -11.780705 -17.923150 -5.638260 0.0000052
## White-Asian -5.984817 -10.959204 -1.010429 0.0107867
## Latino-Asian -3.001086 -10.324175 4.322003 0.7178329
## White-Black 5.795888 1.787167 9.804610 0.0011799
## Latino-Black 8.779619 2.074923 15.484315 0.0042992
## Latino-White 2.983731 -2.670280 8.637742 0.5268555
This analysis is done via One way ANOVA using Dehumanization of Natives as the DV
Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.
First Columbus Day:
Then Indigenous Peoples’ Day:
Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.
First Columbus Day:
Then Indigenous Peoples’ Day:
Now we’re going to take the opportunity to drill down a bit into the question of Native Stereotypes.
This time we’re going to pull the stereotype of Natives as Childlike as well as the stereotype of Natives as Uncivilized.
The way I’m going to do this is to pull both measures out of the dehumanization composite score and look at their effects on our DVs separately. To test this statistically we’ll place them into linear models together with the composite.
Let’s begin!
This first graph will show us regression lines for each of our 4 measures of dehumanization and their relationship with Support for abolishing Columbus Day
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8633 -1.8083 -0.0807 1.7581 4.6669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.783234 0.188753 4.150 3.43e-05 ***
## White_Dehumanize -0.053827 0.004709 -11.431 < 2e-16 ***
## Native_Dehumanize 0.027957 0.004910 5.694 1.37e-08 ***
## DH5_W_Childlike 0.018252 0.001726 10.575 < 2e-16 ***
## DH5_N_Childlike -0.003985 0.001913 -2.082 0.0374 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.041 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.07825, Adjusted R-squared: 0.07693
## F-statistic: 59.03 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5164 -1.3058 0.0292 1.6081 3.8440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.033044 0.176129 -0.188 0.851
## White_Dehumanize -0.038655 0.004394 -8.798 < 2e-16 ***
## Native_Dehumanize 0.034383 0.004581 7.505 8.24e-14 ***
## DH5_W_Childlike 0.013216 0.001610 8.206 3.46e-16 ***
## DH5_N_Childlike -0.007674 0.001785 -4.298 1.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.905 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.05297, Adjusted R-squared: 0.05161
## F-statistic: 38.89 on 4 and 2781 DF, p-value: < 2.2e-16
Now we’ll take a look at stereotypes about being civilized using the same methods as before:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## N_Uncivilized 6 2811 2.28 1.33 2.00 2.10 1.48 1
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## N_Uncivilized 7.00 6.00 1.01 0.39 0.03
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + N_Uncivilized, data = dvstereotypes.civil.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8540 -1.7106 -0.1154 1.6579 4.9535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.616558 0.198675 8.137 6.06e-16 ***
## White_Dehumanize -0.007693 0.005631 -1.366 0.172
## Native_Dehumanize 0.020416 0.004884 4.180 3.00e-05 ***
## DH2_W_Civilized -0.027049 0.002142 -12.629 < 2e-16 ***
## N_Uncivilized -0.210592 0.029076 -7.243 5.66e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.012 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.1045, Adjusted R-squared: 0.1032
## F-statistic: 81.15 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## N_Uncivilized 6 2811 2.28 1.33 2.00 2.10 1.48 1
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## N_Uncivilized 7.00 6.00 1.01 0.39 0.03
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + N_Uncivilized, data = dvstereotypes.civil.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.395 -1.159 0.123 1.544 4.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.810852 0.185547 4.370 1.29e-05 ***
## White_Dehumanize -0.008018 0.005259 -1.525 0.127
## Native_Dehumanize 0.025919 0.004561 5.682 1.47e-08 ***
## DH2_W_Civilized -0.017364 0.002000 -8.680 < 2e-16 ***
## N_Uncivilized -0.252216 0.027155 -9.288 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.879 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.07836, Adjusted R-squared: 0.07703
## F-statistic: 59.11 on 4 and 2781 DF, p-value: < 2.2e-16
The story here seems to be that in the middle, when we’re talking about the average level of dehumanization, people are more or less treating the civilized question like they treat the other measures of dehumanization. However, when it comes to perceptions of Whites and Natives as childlike something else starts happening, particularly at the extremes. Whether there is a story there for These two measure in particular is difficult to say.
Let’s look at these measures broken down by race and then by education level:
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 6503147 1300629 4167.00 < 2e-16 ***
## Race 3 15830 5277 16.91 5.85e-11 ***
## Group:Race 15 35861 2391 7.66 < 2e-16 ***
## Residuals 15320 4781767 312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 52 observations deleted due to missingness
And in case we haven’t done it, here’s the measure’s side by side without separating for race:
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 6503147 1300629 4127 <2e-16 ***
## Residuals 15338 4833458 315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 52 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Rating ~ Group, data = dvstereotypes.race.lng)
##
## $Group
## diff lwr upr
## Native_Dehumanize-White_Dehumanize -1.938569 -3.355904 -0.5212349
## DH2_W_Civilized-White_Dehumanize 13.523152 12.107754 14.9385500
## N_Uncivilized-White_Dehumanize -50.761396 -52.176656 -49.3461354
## DH5_W_Childlike-White_Dehumanize -12.558589 -13.974124 -11.1430529
## DH5_N_Childlike-White_Dehumanize -23.887754 -25.303841 -22.4716664
## DH2_W_Civilized-Native_Dehumanize 15.461721 14.046463 16.8769800
## N_Uncivilized-Native_Dehumanize -48.822827 -50.237948 -47.4077054
## DH5_W_Childlike-Native_Dehumanize -10.620019 -12.035416 -9.2046229
## DH5_N_Childlike-Native_Dehumanize -21.949185 -23.365133 -20.5332363
## N_Uncivilized-DH2_W_Civilized -64.284548 -65.697729 -62.8713662
## DH5_W_Childlike-DH2_W_Civilized -26.081741 -27.495198 -24.6682833
## DH5_N_Childlike-DH2_W_Civilized -37.410906 -38.824916 -35.9968959
## DH5_W_Childlike-N_Uncivilized 38.202807 36.789488 39.6161267
## DH5_N_Childlike-N_Uncivilized 26.873642 25.459770 28.2875141
## DH5_N_Childlike-DH5_W_Childlike -11.329165 -12.743313 -9.9150175
## p adj
## Native_Dehumanize-White_Dehumanize 0.0013639
## DH2_W_Civilized-White_Dehumanize 0.0000000
## N_Uncivilized-White_Dehumanize 0.0000000
## DH5_W_Childlike-White_Dehumanize 0.0000000
## DH5_N_Childlike-White_Dehumanize 0.0000000
## DH2_W_Civilized-Native_Dehumanize 0.0000000
## N_Uncivilized-Native_Dehumanize 0.0000000
## DH5_W_Childlike-Native_Dehumanize 0.0000000
## DH5_N_Childlike-Native_Dehumanize 0.0000000
## N_Uncivilized-DH2_W_Civilized 0.0000000
## DH5_W_Childlike-DH2_W_Civilized 0.0000000
## DH5_N_Childlike-DH2_W_Civilized 0.0000000
## DH5_W_Childlike-N_Uncivilized 0.0000000
## DH5_N_Childlike-N_Uncivilized 0.0000000
## DH5_N_Childlike-DH5_W_Childlike 0.0000000
What we end up with is several possible stories. The big picture seems to be that in some cases it is the measure that is differentiating and other times the race of the participant seems to be driving differences, particularly in our civilized and childlike measures.
Now, let’s take a bit of a left turn and introduce another wrinkle into this analysis. How do these things break down by education level. For the sake of argument we’ll look just at college educated versus those who are not college educated.
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 6979166 1395833 4369.986 < 2e-16 ***
## College 1 1310 1310 4.101 0.0429 *
## Group:College 5 21309 4262 13.343 5.25e-13 ***
## Residuals 16717 5339638 319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 689 observations deleted due to missingness
A simple interaction plot will show us these effects in a different form:
This will be where we put other questions and answers that we have.
EMP_comp:
| vars | n | mean | sd | median | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 1 | 2811 | 61.24 | 21.82 | 62 |
| DH2_W_Civilized | 2 | 2810 | 65.89 | 23.38 | 69 |
| DH3_W_Moral | 3 | 2812 | 55.77 | 22.76 | 55 |
| DH4_W_Polite | 4 | 2812 | 57.99 | 22.53 | 58 |
| DH5_W_Childlike | 5 | 2809 | 40.76 | 24.49 | 43 |
| DH6_W_Rational | 6 | 2810 | 58.23 | 22.49 | 57 |
| DH7_W_Warm | 7 | 2810 | 58.27 | 22.32 | 58 |
| DH8_W_Agentic | 8 | 2800 | 46.11 | 22.34 | 50 |
| DH9_W_Refined | 9 | 2809 | 52.87 | 22.26 | 52 |
| DH10_W_Lacking_Culture | 10 | 2808 | 46.34 | 27.71 | 50 |
| DH11_W_Lacking_Self-restraint | 11 | 2808 | 48.59 | 25.58 | 50 |
| DH12_W_Instinctual | 12 | 2810 | 51.47 | 22.98 | 51 |
| DH13_W_Mature | 13 | 2809 | 57.34 | 22.31 | 56 |
| DH14_W_Stoic | 14 | 2808 | 42.88 | 22.31 | 49 |
| DH15_W_Emotionally_Responsive | 15 | 2812 | 59.98 | 22.77 | 60 |
| DH16_W_Cold | 16 | 2810 | 44.74 | 23.99 | 49 |
| DH17_W_Open | 17 | 2811 | 55.59 | 22.81 | 55 |
| DH18_W_Rigid | 18 | 2807 | 49.6 | 23.27 | 50 |
| DH19_W_Passive | 19 | 2810 | 45.16 | 23.21 | 49 |
| DH20_W_Superficial | 20 | 2810 | 59.32 | 25.54 | 60 |
| DH1_N_Responsible | 21 | 2807 | 63.86 | 21.06 | 64 |
| N_Uncivilized | 22 | 2811 | 2.277 | 1.33 | 2 |
| DH3_N_Moral | 23 | 2807 | 65.75 | 20.88 | 66 |
| DH4_N_Polite | 24 | 2806 | 63.22 | 21.42 | 63 |
| DH5_N_Childlike | 25 | 2802 | 29.22 | 22.26 | 25 |
| DH6_N_Rational | 26 | 2807 | 61.61 | 21.17 | 61 |
| DH7_N_Warm | 27 | 2804 | 61.05 | 22.07 | 60 |
| DH8_N_Agentic | 28 | 2800 | 44.97 | 22.32 | 50 |
| DH9_N_Refined | 29 | 2806 | 51.11 | 22.44 | 51 |
| DH10_N_Lacking_Culture | 30 | 2804 | 23.9 | 23.15 | 17 |
| DH11_N_Lacking_Self-restraint | 31 | 2805 | 34.44 | 23.39 | 33 |
| DH12_N_Instinctual | 32 | 2807 | 61.3 | 23.48 | 61 |
| DH13_N_Mature | 33 | 2806 | 65.15 | 20.69 | 65 |
| DH14_N_Stoic | 34 | 2808 | 59.11 | 22.29 | 57 |
| DH15_N_Emotionally_Responsive | 35 | 2806 | 54.38 | 24.29 | 53 |
| DH16_N_Cold | 36 | 2805 | 35.19 | 22.8 | 35 |
| DH17_N_Open | 37 | 2805 | 53.8 | 23.34 | 53 |
| DH18_N_Rigid | 38 | 2807 | 45.41 | 23.89 | 50 |
| DH19_N_Passive | 39 | 2805 | 45.03 | 23.79 | 50 |
| DH20_N_Superficial | 40 | 2805 | 30.41 | 22.87 | 27 |
| EMP* | 41 | 2903 | 1 | 0 | 1 |
| Mean Score | 42 | 2841 | 3.337 | 1.438 | 3.4 |
| trimmed | mad | min | max | range | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 62.64 | 19.27 | 0 | 100 | 100 |
| DH2_W_Civilized | 67.81 | 25.2 | 0 | 100 | 100 |
| DH3_W_Moral | 56.82 | 22.24 | 0 | 100 | 100 |
| DH4_W_Polite | 59.29 | 20.76 | 0 | 100 | 100 |
| DH5_W_Childlike | 39.98 | 26.69 | 0 | 100 | 100 |
| DH6_W_Rational | 59.45 | 20.76 | 0 | 100 | 100 |
| DH7_W_Warm | 59.58 | 19.27 | 0 | 100 | 100 |
| DH8_W_Agentic | 46.85 | 11.86 | 0 | 100 | 100 |
| DH9_W_Refined | 53.56 | 20.76 | 0 | 100 | 100 |
| DH10_W_Lacking_Culture | 45.92 | 32.62 | 0 | 100 | 100 |
| DH11_W_Lacking_Self-restraint | 48.69 | 28.17 | 0 | 100 | 100 |
| DH12_W_Instinctual | 52.05 | 20.76 | 0 | 100 | 100 |
| DH13_W_Mature | 58.44 | 20.76 | 0 | 100 | 100 |
| DH14_W_Stoic | 42.9 | 20.76 | 0 | 100 | 100 |
| DH15_W_Emotionally_Responsive | 61.27 | 20.76 | 0 | 100 | 100 |
| DH16_W_Cold | 44.43 | 25.2 | 0 | 100 | 100 |
| DH17_W_Open | 56.57 | 22.24 | 0 | 100 | 100 |
| DH18_W_Rigid | 50.06 | 22.24 | 0 | 100 | 100 |
| DH19_W_Passive | 45.16 | 23.72 | 0 | 100 | 100 |
| DH20_W_Superficial | 60.99 | 25.2 | 0 | 100 | 100 |
| DH1_N_Responsible | 64.78 | 20.76 | 0 | 100 | 100 |
| N_Uncivilized | 2.105 | 1.483 | 1 | 7 | 6 |
| DH3_N_Moral | 66.67 | 22.24 | 0 | 100 | 100 |
| DH4_N_Polite | 64.15 | 19.27 | 0 | 100 | 100 |
| DH5_N_Childlike | 27.68 | 28.17 | 0 | 100 | 100 |
| DH6_N_Rational | 62.6 | 17.79 | 0 | 100 | 100 |
| DH7_N_Warm | 61.99 | 20.76 | 0 | 100 | 100 |
| DH8_N_Agentic | 45.62 | 11.86 | 0 | 100 | 100 |
| DH9_N_Refined | 51.46 | 19.27 | 0 | 100 | 100 |
| DH10_N_Lacking_Culture | 20.99 | 22.24 | 0 | 100 | 100 |
| DH11_N_Lacking_Self-restraint | 33.22 | 26.69 | 0 | 100 | 100 |
| DH12_N_Instinctual | 62.82 | 20.76 | 0 | 100 | 100 |
| DH13_N_Mature | 66.08 | 22.24 | 0 | 100 | 100 |
| DH14_N_Stoic | 60.12 | 20.76 | 0 | 100 | 100 |
| DH15_N_Emotionally_Responsive | 55.39 | 22.24 | 0 | 100 | 100 |
| DH16_N_Cold | 34.35 | 25.2 | 0 | 100 | 100 |
| DH17_N_Open | 54.26 | 22.24 | 0 | 100 | 100 |
| DH18_N_Rigid | 45.67 | 23.72 | 0 | 100 | 100 |
| DH19_N_Passive | 44.94 | 22.24 | 0 | 100 | 100 |
| DH20_N_Superficial | 28.75 | 29.65 | 0 | 100 | 100 |
| EMP* | 1 | 0 | 1 | 1 | 0 |
| Mean Score | 3.306 | 1.483 | 1 | 7 | 6 |
| skew | kurtosis | se | |
|---|---|---|---|
| DH1_W_Responsible | -0.5742 | 0.2696 | 0.4116 |
| DH2_W_Civilized | -0.6889 | 0.2007 | 0.4411 |
| DH3_W_Moral | -0.3719 | -0.1342 | 0.4292 |
| DH4_W_Polite | -0.4732 | 0.009527 | 0.4248 |
| DH5_W_Childlike | 0.2092 | -0.5542 | 0.462 |
| DH6_W_Rational | -0.4488 | 0.001843 | 0.4243 |
| DH7_W_Warm | -0.4832 | 0.07056 | 0.421 |
| DH8_W_Agentic | -0.3413 | 0.1309 | 0.4222 |
| DH9_W_Refined | -0.2509 | -0.1186 | 0.4199 |
| DH10_W_Lacking_Culture | 0.07817 | -0.8826 | 0.523 |
| DH11_W_Lacking_Self-restraint | -0.04029 | -0.7017 | 0.4828 |
| DH12_W_Instinctual | -0.2033 | -0.2257 | 0.4335 |
| DH13_W_Mature | -0.4135 | 0.04243 | 0.421 |
| DH14_W_Stoic | -0.04162 | -0.298 | 0.4209 |
| DH15_W_Emotionally_Responsive | -0.4721 | 0.002383 | 0.4294 |
| DH16_W_Cold | 0.09141 | -0.5112 | 0.4525 |
| DH17_W_Open | -0.3409 | -0.1675 | 0.4302 |
| DH18_W_Rigid | -0.1571 | -0.3567 | 0.4393 |
| DH19_W_Passive | 0.002147 | -0.3755 | 0.4378 |
| DH20_W_Superficial | -0.4857 | -0.3722 | 0.4818 |
| DH1_N_Responsible | -0.4596 | 0.317 | 0.3976 |
| N_Uncivilized | 1.006 | 0.3926 | 0.02509 |
| DH3_N_Moral | -0.527 | 0.4136 | 0.3941 |
| DH4_N_Polite | -0.4299 | 0.2116 | 0.4044 |
| DH5_N_Childlike | 0.5351 | -0.4322 | 0.4205 |
| DH6_N_Rational | -0.4534 | 0.336 | 0.3995 |
| DH7_N_Warm | -0.3667 | 0.02378 | 0.4167 |
| DH8_N_Agentic | -0.3395 | -0.007707 | 0.4219 |
| DH9_N_Refined | -0.1243 | -0.06897 | 0.4235 |
| DH10_N_Lacking_Culture | 0.978 | 0.2417 | 0.4371 |
| DH11_N_Lacking_Self-restraint | 0.3627 | -0.4915 | 0.4416 |
| DH12_N_Instinctual | -0.5158 | 0.04848 | 0.4433 |
| DH13_N_Mature | -0.5208 | 0.4368 | 0.3907 |
| DH14_N_Stoic | -0.3809 | 0.0361 | 0.4206 |
| DH15_N_Emotionally_Responsive | -0.3216 | -0.2435 | 0.4585 |
| DH16_N_Cold | 0.2317 | -0.6249 | 0.4306 |
| DH17_N_Open | -0.1702 | -0.3199 | 0.4406 |
| DH18_N_Rigid | -0.1095 | -0.5238 | 0.451 |
| DH19_N_Passive | 0.006644 | -0.4618 | 0.4492 |
| DH20_N_Superficial | 0.543 | -0.3894 | 0.4318 |
| EMP* | NA | NA | 0 |
| Mean Score | 0.1451 | -0.5044 | 0.02698 |